Combining alcohol interventions with tobacco addictions treatment in primary care—the COMBAT study: a pragmatic cluster randomized trial
Bibliographic record
Abstract
BACKGROUND: Tobacco and alcohol use present multiplicative risk for aerodigestive cancers. Reducing alcohol consumption improves smoking cessation outcomes and reduces cancer risk. Risky alcohol consumption and smoking are often treated separately despite concurrent treatment potentially leading to better outcomes for each. However, no rapidly scalable program exists for combined interventions in primary care clinics spread across wide geographic areas. This cluster randomized trial aims to report on the effects of a novel clinical decision support system (CDSS) on intervention rates by primary care practitioners addressing risky alcohol use in a smoking cessation program. METHODS/DESIGN: We will be implementing a clinical decision support system (CDSS) in 221 primary care sites participating in the Smoking Treatment for Ontario Patients (STOP) program across Ontario, Canada. Sites will be blindly allocated to one of two clinical decision support systems guiding practitioners to provide a risky alcohol use intervention to smokers attempting to quit using nicotine replacement therapy (NRT). Risky alcohol use is defined as drinking above the Canadian Cancer Society's low-risk drinking guidelines. Primary analysis will measure the proportion of risky drinkers offered an alcohol intervention in each CDSS arm at baseline. Patients will be contacted by phone or email to track smoking cessation and alcohol consumption rates at 6- and 12-month follow-up. DISCUSSION: Upon completion of the trial, the effect of different clinical decision support systems on practitioner behaviour, and on client tobacco and alcohol use, will be discussed. If the CDSS successfully promotes SBIRT for risky alcohol use in a primary care setting and/or improves patient-level outcomes, including smoking cessation rates and alcohol use reduction, this tool can be used as a model for other web-based behaviour change interventions integrated into primary care practice. TRIAL REGISTRATION: ClinicalTrials.gov NCT03108144.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".